code: def do_train(
      learning_rate=0.001,
      dropout=0.1,
      conv1=32,
      conv2=64,
      save_model=False,
      folder_name=None,
  ):
      model = get_model(**locals())
  
      model.fit(
          x_train,
          y_train,
          batch_size=batch_size,
          epochs=num_epochs,
          verbose=1,
          validation_split=0.5,
      )
  
      res = model.evaluate(
          x_test,
          y_test,
          batch_size=batch_size,
          verbose=1,
      )
  
      # this means we are in best job context
      # config also contains save_model flag and
      # folder_name path
      if save_model is True:
          import shutil
          import os
  
          path = os.path.join("aup_models", folder_name)
  
          if os.path.exists("aup_models") is False:
              os.makedirs("aup_models")
  
          if os.path.exists(path) is True:
              shutil.rmtree(path)
  
          os.makedirs(path)
          os.chdir(path)
  
          save_model_fun(model)
  
      flops = get_flops()
  
      return (res[1] - 1.0) / log(flops)
  
file_path: auptimizer/Examples/mnist_keras_save_model/mnist_wo_decorator.py
imports: from aup import BasicConfig, print_result
  import tensorflow
  import numpy
  from tensorflow import keras
  from tensorflow.keras import layers
  from math import log
  import sys
label_desc: Use image node_id 3 for calling a global function with example usage: do_train(learning_rate, dropout, conv1, conv2, save_model, folder_name) and returns: unknown